EP3882813A1 - Procédé de génération d'un réseau d'occupation dynamique - Google Patents
Procédé de génération d'un réseau d'occupation dynamique Download PDFInfo
- Publication number
- EP3882813A1 EP3882813A1 EP20164443.2A EP20164443A EP3882813A1 EP 3882813 A1 EP3882813 A1 EP 3882813A1 EP 20164443 A EP20164443 A EP 20164443A EP 3882813 A1 EP3882813 A1 EP 3882813A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- host vehicle
- cells
- lane
- occupancy grid
- reference line
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000001514 detection method Methods 0.000 claims description 17
- 230000000007 visual effect Effects 0.000 claims description 10
- 238000013500 data storage Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/10—Number of lanes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2013/9323—Alternative operation using light waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2013/9327—Sensor installation details
- G01S2013/93271—Sensor installation details in the front of the vehicles
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/165—Anti-collision systems for passive traffic, e.g. including static obstacles, trees
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
Definitions
- the present disclosure relates to a method for generating a dynamic occupancy grid in front of a host vehicle.
- a so-called occupancy grid technique which analyzes the state of cells defined by a grid in front of the vehicle, i.e. if each cell is occupied, partly occupied, occluded or free.
- the occupancy grid is usually designed on the basis of a vehicle coordinate system wherein all cells of the grid have the same size and the same orientation within this coordinate system. That is, regarding the vehicle coordinate system the cells of the conventional occupancy grid are position invariant and time invariant.
- the present disclosure provides a computer implemented method, a computer system and a non-transitory computer readable medium according to the independent claims. Embodiments are given in the subclaims, the description and the drawings.
- the present disclosure is directed at a computer implemented method for generating a dynamic occupancy grid in front of a host vehicle.
- an indicator is detected for the course of a lane of a road in front of the host vehicle via a detection system of the host vehicle, and a base area is determined based on the detected indicator via a computer system of the host vehicle, wherein the base area is restricted to a region of interest in front of the host vehicle.
- a plurality of cells is defined via the computer system by dividing the base area in order to form the occupancy grid. For each cell of the occupancy grid, it is determined whether the cell is occupied at least partly by a detectable object via the computer system based on data provided by the detection system.
- the detection system of the host vehicle may include a visual system, a radar system and/or a LIDAR system, whereas the computer system may include a processor and a memory. Since the base area is determined based on the indicator for the course of the lane, the base area which defines the dimensions and the number of the cells of the grid covers the region of interest in front of the vehicle only, i.e. including the lane and certain areas on the right and left sides of the lane. Therefore, the lateral size of the grid, i.e. perpendicular to the course of the lane, is decreased in comparison to a conventional occupancy grid which leads to a less number of cells which have to be taken into account.
- the resolution of the grid i.e. the number of cells per unit area
- the resolution of the grid may be increased without requiring additional memory.
- the number of useless data generated by cells being located outside the region of interest is strongly reduced for a dynamic grid adapted to the course of the lane in comparison to a conventional occupancy grid.
- the data provided by the dynamic occupancy grid are more suitable for further vehicle systems which rely on or use this data since the number of useless data provided by the grid is strongly reduced and the transferred data is therefore preprocessed regarding relevance.
- the performance of the further vehicle systems using the dynamic occupancy grid is improved, especially by reducing the total amount of transferred data.
- a reference line may be defined via the computer system along the lane based on the indicator, and the base area may be determined on both sides of the reference line.
- the reference line may be divided into segments, wherein for each of the segments a respective row of cells may be defined perpendicularly to the reference line. For each row of cells, a predetermined cell width and/or a predetermined number of cells may be defined.
- two respective straight lines may be defined perpendicularly to the reference line at a beginning and at an end of the segment, respectively, and each straight line may be divided into a predefined number of sections, wherein end points of the respective sections may define corners of a respective one of the plurality of cells.
- the segments of the reference line may have a predefined length.
- the segments of the reference line may have a variable length, wherein the variable length may depend on a distance of the segment with respect to the host vehicle. The variable length of the segments may increase when the distance of the segment with respect to the host vehicle increases.
- a reference line may be defined via the computer system along the lane based on the indicator, and the base area may be determined on both sides of the reference line.
- the definition of the reference line for example in the middle of the lane, may be used for a straightforward adaptation of the base area to the course of the lane which is given by the indicator. Since the indicator may be measured e.g. by the detection system of the host vehicle, the reference line may be a straightforward means in order to "dynamically deform" the base area such that it reflects the course of the lane.
- the reference line may be represented by a polygon being adapted to measured indicator data. The use of such a reference line may further reduce the computational effort which is required to perform the method.
- a predetermined cell width may be defined, and alternatively or additionally, a predetermined number of cells may be defined.
- the predetermined cell width and the predetermined number of cells may include constant values.
- two respective straight lines may be defined perpendicularly to the reference line at a beginning and at an end of the segment, respectively.
- Each straight line may be further divided into a predefined number of sections. End points of the respective sections may define corners of a respective one of the plurality of cells.
- the predefined number of sections for each straight line perpendicular to the reference line may be a representation for the lateral extension of the dynamic occupancy grid which follows the course of the lane from one straight line to the next.
- the definition of the corners for the respective cells of the occupancy grid by using the end points of the sections may further facilitate generating the dynamic occupancy grid and may reduce the required computational effort.
- the segments of the reference line may have a predefined length. In this case, generating the occupancy grid may be facilitated again, and the computational effort may be further reduced.
- the segments of the reference line may have a variable length which may depend on a distance of the segment with respect to the host vehicle. In detail, the length of the segments may increase when the distance of the segment with respect to the host vehicle increases.
- the size of the cells may be adapted within the region of interest by using a variable length of the segments. For example, it may be desired to have an increased resolution of the occupancy grid close to the host vehicle, and for regions having still a greater distance with respect to the host vehicle, less resolution is required e.g. for a proper performance of further vehicle systems like advanced driver assistance systems. Since a dynamic occupancy grid being restricted to the region of interest for the host vehicle and following the course of the lane in front of the host vehicle may include much less data and may need less computational effort than a conventional occupancy grid, the resolution of the grid may be increased within certain parts of the region of interest without extensively increasing the computational effort and therefore without sacrificing the most important advantages of the dynamic occupancy grid.
- the indicator for the course of the lane may include right and/or left margins of the lane and, alternatively or additionally, markers for the center of the lane.
- available landmarks may be used as indicator for the course of the lane which may be easily detected by systems which are available anyway in the host vehicle. Therefore, the detection of the indicator including these landmarks, i.e. margins of the lane and/or markers for its center, may be performed without generating additional cost.
- the detection system of the host vehicle for detecting the indicator may include a visual system and/or a radar system of the host vehicle.
- the visual system and the radar system are examples for systems which are available anyway in nowadays vehicles.
- the base area may be determined in accordance with a predetermined range of the visual system and/or of the radar system. That is, the base area for generating the dynamic occupancy grid may be restricted longitudinally, i.e. along the lane in front of the host vehicle, by a predetermined and known range of the visual system and/or the radar system which may provide the indicator for the course of the lane in order to determine the base area and to define the dynamic occupancy grid.
- the dynamic occupancy grid may therefore be generated based on a predetermined length along the lane in front of the vehicle. This may again facilitate the overall generation of the dynamic occupancy grid.
- the present disclosure is directed at a computer system, said computer system being configured to carry out several or all steps of the computer implemented method described herein.
- the computer system may comprise a processing unit, at least one memory unit and at least one non-transitory data storage.
- the non-transitory data storage and/or the memory unit may comprise a computer program for instructing the computer to perform several or all steps or aspects of the computer implemented method described herein.
- the present disclosure is directed at a non-transitory computer readable medium comprising instructions for carrying out several or all steps or aspects of the computer implemented method described herein.
- the computer readable medium may be configured as: an optical medium, such as a compact disc (CD) or a digital versatile disk (DVD); a magnetic medium, such as a hard disk drive (HDD); a solid state drive (SSD); a read only memory (ROM), such as a flash memory; or the like.
- the computer readable medium may be configured as a data storage that is accessible via a data connection, such as an internet connection.
- the computer readable medium may, for example, be an online data repository or a cloud storage.
- the present disclosure is also directed at a computer program for instructing a computer to perform several or all steps or aspects of the computer implemented method described herein.
- Fig. 1 schematically depicts a host vehicle 11 and a target vehicle 13 driving in the same lane 15 in front of the host vehicle 11.
- Fig. 1 further depicts a schematic representation of an occupancy grid 17 according to the background art which is defined with respect to a vehicle coordinate system 19 which is a Cartesian coordinate system including an x-axis 21 extending in a lateral direction with respect to the vehicle 11 and a y-axis 23 extending in a longitudinal direction in front of the host vehicle 11.
- the occupancy grid 17 includes a plurality of cells 25 which have a fixed dimension along the x-axis 21 and along the y-axis 23. That is, the length and the width of each cell 25 are predefined.
- the number of cells 25 of the occupancy grid 17 is also predetermined in the lateral and longitudinal directions, i.e. along the x-axis 21 and along the y-axis 23. That is, the occupancy grid 17 according to the background art always includes a fixed number of cells 25 in longitudinal direction and may include a predefined number of cells 25 in lateral direction, as shown in Fig. 1 , regardless of the course of the lane 15 or the movement of the host vehicle 11.
- the existence and the position of the target vehicle 13 are detected by a detection system 27 of the host vehicle 11.
- the detection system 27 includes a visual system and/or a radar system.
- the detection system 27 is also configured to detect a left margin 29 and a right margin 31 of the lane 15.
- the host vehicle 11 further includes a computer system 28 which includes a processor and a memory and which is configured to generate the occupancy grid 17.
- the occupancy grid 17 includes many cells 25 which are far away from the lane 15 in which the host vehicle 11 and the target vehicle 13 are driving momentarily. That is, the occupancy grid 17 according to the background art usually includes a huge number of cells which are outside a region of interest for the actual movement of the host vehicle 11 and for the systems of the host vehicle 11 which rely on or use the data being provided by the occupancy grid 17.
- the occupancy grid 17 usually includes a huge number of cells which are outside a region of interest for the actual movement of the host vehicle 11 and for the systems of the host vehicle 11 which rely on or use the data being provided by the occupancy grid 17.
- an object or obstacle detected by the detection system 27 it is determined whether an object or obstacle detected by the detection system 27 is present or not. In other words, it is determined for each cell 25 whether it is occupied by an obstacle or object.
- the cells 25 which are covered at least partly by the target vehicle 13 are regarded as occupied.
- the region of interest which is relevant for the host vehicle 11 includes the lane 15 per se and some area beyond the left margin 29 and the right margin 31 within the close vicinity of the lane 15. Therefore, the occupancy grid 17 according to the background art includes a huge number of cells and unnecessary data from the regions outside the region of interest. This unnecessary data is usually processed by the computer system 28 of the host vehicle 11 and requires a corresponding unnecessary memory and processor performance. That is, usage of memory and processors of the host vehicle 11 are unnecessarily wasted when using an occupancy grid 17 according to the background art.
- Fig. 2 depicts a schematic representation of a dynamic occupancy grid 33 according to the disclosure.
- the dynamic occupancy grid 33 includes a plurality of dynamic cells 35 and is adapted to the course of the lane 15 in front of the host vehicle 11.
- the dynamic occupancy grid 33 is defined via the computer system 28 of the host vehicle 11 for a base area 37 which corresponds to a region of interest in front of the host vehicle 11.
- the left margin 29 and the right margin 31 of the lane 15 are detected by the detection system 27 of the host vehicle 11. Since the left margin 29 and the right margin 31 limit the lane 15, the left and right margins 29, 31 are used as indicators for the course of the lane 15 in front of the host vehicle 11.
- the base area 37 for the dynamic occupancy grid 33 is intended to cover the region of interest for the host vehicle 11. For covering this region of interest properly, some areas beyond the left margin 29 and beyond the right margin 31 are included in the base area 37. That is, some parts of adjacent lanes, sidewalks and/or further environment like ditches may also be relevant for the further movement of the host vehicle 11 and have therefore to be included into the base area 37.
- the base area 37 is further divided in a plurality of dynamic cells 35 in order to generate the dynamic occupancy grid 33.
- the total number of cells 35 is strongly reduced in comparison to the total number of cells 25 of the conventional objective grid 17 without losing relevant information which is provided by the grids 17, 33 and which is relevant for e.g. the assistance systems of the host vehicle 11. This especially holds true if the lane 15 includes a strong curvature.
- the number of unnecessary cells and data and therefore unnecessary computational effort are strongly reduced when using the dynamic occupancy grid 33 reflecting the course of the lane 15 in comparison to the conventional objective grid 17.
- Fig. 3 depicts in detail how the dynamic cells 35 of the dynamic occupancy grid 33 are generated via the computer system 28 of the host vehicle 11.
- a reference line 41 is defined which extends approximately in the center of the lane 15 in which the host vehicle 11 and the target vehicle 13 are driving momentarily.
- the reference line 41 is represented by a polynomial whose coefficients are derived from an indicator for the course of the lane 15 which is measured by the detection system 27 of the host vehicle 11, e.g. by measuring the course of the left margin 29 and the right margin 31 of the lane 15 as indicators for the course of the lane 15.
- the reference line 41 represented by the polynomial is divided into a plurality of segments 43 having a constant length ⁇ along the reference line 41.
- segments 43 having a constant length ⁇ along the reference line 41.
- two straight lines 45 are defined extending perpendicularly to the reference line 41, respectively. That is, adjacent segments 43 have a common straight line 45 which delimits respective areas from each other which extend on both sides of the reference line 41 between the straight lines 45.
- the straight lines 45 are further divided into sections 47 having a constant length ⁇ . Therefore, end points 49 of the respective sections 47 also have a constant distance ⁇ from each other.
- the end points 49 of the sections 47 are used in order to define corner points for a respective dynamic cell 35 (see Fig. 2 ).
- two end points 49 of a section 47 being adjacent to each other and belonging to a first straight line 45 define two corner points of a dynamic cell 35
- two further end points 49 of a section 47 of the adjacent straight line 45 having the shortest distances to the first straight line 45 define two further corner points for the dynamic cell 35. That is, the four corner points of each dynamic cell 35 are defined by respective end points 49 of sections 47 belonging to adjacent straight lines 45 and having the shortest distance with respect to each other.
- the size of the dynamic cells 35 varies within the dynamic occupancy grid 33, as can be recognized in Fig. 2 .
- the length of the segments 43 may be varied as an alternative along the reference line 41. For example, close to the host vehicle 11 a short length of the segments 43 may be used, whereas the length of the segments 43 may increase when their distance increases with respect to the host vehicle 11.
- each segment 43 defines a row of dynamic cells 35, wherein this row extends perpendicularly to the reference line 41. If a predefined number of cells 35 is used for each row of cells 35 belonging to a certain segment 43, a constant lateral width of the dynamic occupancy grid 33 is defined corresponding to a constant lateral extension of the base area 37 corresponding to and covering the region of interest in front of the host vehicle 11.
- the number of cells 35 for each row may be adjusted to the curvature of the lane 15 and the reference line 41.
- a greater number of cells 35 may be considered on a first side to which the reference line 41 is curved, e.g. on the right side as shown in Figs. 2 and 3 , whereas a smaller number of cells 35 is taken into account on the second side from which the reference line 41 departs.
- Fig. 2 Such a situation is shown in Fig. 2 in which more cells 35 are present at the "inner side" of the lane 15 beyond the right margin 31, whereas less cells 35 are considered at the "outer side" of the left margin 29 of the lane 15.
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- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
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Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP20164443.2A EP3882813A1 (fr) | 2020-03-20 | 2020-03-20 | Procédé de génération d'un réseau d'occupation dynamique |
EP21157127.8A EP3882815B1 (fr) | 2020-03-20 | 2021-02-15 | Procédé de génération d'un réseau dynamique |
US17/196,919 US11662461B2 (en) | 2020-03-20 | 2021-03-09 | Method for generating a dynamic occupancy grid |
CN202110289238.0A CN113495272B (zh) | 2020-03-20 | 2021-03-18 | 生成动态占用网格的方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP20164443.2A EP3882813A1 (fr) | 2020-03-20 | 2020-03-20 | Procédé de génération d'un réseau d'occupation dynamique |
Publications (1)
Publication Number | Publication Date |
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EP3882813A1 true EP3882813A1 (fr) | 2021-09-22 |
Family
ID=69941187
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
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EP20164443.2A Pending EP3882813A1 (fr) | 2020-03-20 | 2020-03-20 | Procédé de génération d'un réseau d'occupation dynamique |
EP21157127.8A Active EP3882815B1 (fr) | 2020-03-20 | 2021-02-15 | Procédé de génération d'un réseau dynamique |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
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EP21157127.8A Active EP3882815B1 (fr) | 2020-03-20 | 2021-02-15 | Procédé de génération d'un réseau dynamique |
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US (1) | US11662461B2 (fr) |
EP (2) | EP3882813A1 (fr) |
CN (1) | CN113495272B (fr) |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP3882813A1 (fr) | 2020-03-20 | 2021-09-22 | Aptiv Technologies Limited | Procédé de génération d'un réseau d'occupation dynamique |
EP3905106A1 (fr) | 2020-04-27 | 2021-11-03 | Aptiv Technologies Limited | Procédé de détermination d'une zone carrossable |
EP3905105A1 (fr) | 2020-04-27 | 2021-11-03 | Aptiv Technologies Limited | Procédé pour déterminer un espace exempt de collision |
US11794784B1 (en) * | 2021-09-30 | 2023-10-24 | Zoox, Inc. | Conformal path grid |
CN113936481B (zh) * | 2021-12-02 | 2022-07-26 | 公安部交通管理科学研究所 | 一种信号控制交叉口的空间利用状况评价方法 |
DE102022212875A1 (de) * | 2022-11-30 | 2024-06-06 | Zf Friedrichshafen Ag | Verfahren, Steuergerät und Computerprogramm zum positionsbasierten Ermitteln eines interessierenden Bereichs, und computerlesbares Speichermedium |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150353083A1 (en) * | 2013-01-14 | 2015-12-10 | Robert Bosch Gmbh | Creation of an obstacle map |
US20160116916A1 (en) * | 2013-06-03 | 2016-04-28 | Robert Bosch Gmbh | Occupancy grid map for a vehicle |
Family Cites Families (73)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6911488B2 (en) | 2000-09-27 | 2005-06-28 | Shamrock Technologies, Inc. | Physical methods of dispersing characteristic use particles and compositions thereof |
US7124027B1 (en) | 2002-07-11 | 2006-10-17 | Yazaki North America, Inc. | Vehicular collision avoidance system |
US7266477B2 (en) | 2005-06-22 | 2007-09-04 | Deere & Company | Method and system for sensor signal fusion |
JP2009120116A (ja) | 2007-11-16 | 2009-06-04 | Hitachi Ltd | 車両衝突回避支援装置 |
DE102009009047A1 (de) | 2009-02-16 | 2010-08-19 | Daimler Ag | Verfahren zur Objektdetektion |
DE102009022588A1 (de) | 2009-05-26 | 2010-12-02 | GM Global Technology Operations, Inc., Detroit | Verfahren und System zur Überwachung eines Umgebungsbereichs eines Fahrzeugs |
JP5206752B2 (ja) | 2010-08-30 | 2013-06-12 | 株式会社デンソー | 走行環境認識装置 |
JP5761360B2 (ja) * | 2011-10-03 | 2015-08-12 | トヨタ自動車株式会社 | 車両の運転支援システム |
DE102012002695B4 (de) | 2012-02-14 | 2024-08-01 | Zf Cv Systems Hannover Gmbh | Verfahren zur Ermittlung einer Notbremssituation eines Fahrzeuges |
DE102013001228A1 (de) | 2013-01-25 | 2014-07-31 | Wabco Gmbh | Verfahren zum Ermitteln eines Auslösekriteriums für eine Bremsung und Notbremssystem für ein Fahrzeug |
US8930060B1 (en) | 2013-07-15 | 2015-01-06 | Ford Global Technologies | Post-impact path assist for vehicles |
CN103559791B (zh) | 2013-10-31 | 2015-11-18 | 北京联合大学 | 一种融合雷达和ccd摄像机信号的车辆检测方法 |
US9470777B2 (en) | 2014-09-19 | 2016-10-18 | Delphi Technologies, Inc. | Radar system for automated vehicle with phase change based target catagorization |
US11222438B2 (en) | 2016-05-27 | 2022-01-11 | Kabushiki Kaisha Toshiba | Information processing apparatus, vehicle, and information processing method for presence probability of object |
US11132611B2 (en) | 2016-05-27 | 2021-09-28 | Kabushiki Kaisha Toshiba | Information processing apparatus and information processing method for determining presence probability of object |
EP3252658B1 (fr) | 2016-05-30 | 2021-08-11 | Kabushiki Kaisha Toshiba | Appareil de traitement d'informations et procédé de traitement d'informations |
DE102016007630A1 (de) | 2016-06-23 | 2017-12-28 | Wabco Gmbh | Verfahren zum Ermitteln einer Notbremssituation eines Fahrzeuges sowie Vorrichtung zur Durchführung des Verfahrens |
WO2018125848A1 (fr) * | 2016-12-30 | 2018-07-05 | DeepMap Inc. | Génération d'itinéraire à l'aide de cartes haute définition pour véhicules autonomes |
US10545029B2 (en) | 2016-12-30 | 2020-01-28 | DeepMap Inc. | Lane network construction using high definition maps for autonomous vehicles |
JP6804991B2 (ja) | 2017-01-05 | 2020-12-23 | 株式会社東芝 | 情報処理装置、情報処理方法、および情報処理プログラム |
EP3364534B1 (fr) | 2017-02-17 | 2019-09-18 | General Electric Technology GmbH | Améliorations apportées ou se rapportant aux circuits d'attaque de grille |
US20180239969A1 (en) | 2017-02-23 | 2018-08-23 | Ford Global Technologies, Llc | Free Space Detection Using Monocular Camera and Deep Learning |
JP2018138402A (ja) | 2017-02-24 | 2018-09-06 | マツダ株式会社 | 車両運転支援システム及び方法 |
GB2560322B (en) | 2017-03-06 | 2022-02-16 | Jaguar Land Rover Ltd | Control apparatus and method for controlling operation of a component |
US10430641B2 (en) | 2017-03-08 | 2019-10-01 | GM Global Technology Operations LLC | Methods and systems for object tracking using bounding boxes |
US10656248B2 (en) | 2017-05-11 | 2020-05-19 | GM Global Technology Operations LLC | Radar post processing for sidelobe suppression |
US20180350086A1 (en) | 2017-05-31 | 2018-12-06 | Qualcomm Incorporated | System And Method Of Dynamically Filtering Depth Estimates To Generate A Volumetric Map Of A Three-Dimensional Environment Having An Adjustable Maximum Depth |
JP6808590B2 (ja) | 2017-08-08 | 2021-01-06 | 株式会社東芝 | 情報処理装置、情報処理方法、プログラムおよび移動体 |
DE102017217972A1 (de) | 2017-10-10 | 2019-04-11 | Robert Bosch Gmbh | Verfahren und Vorrichtung zum Erzeugen eines inversen Sensormodells und Verfahren zum Erkennen von Hindernissen |
DE102017126388B4 (de) | 2017-11-10 | 2020-12-03 | Valeo Schalter Und Sensoren Gmbh | Verfahren zur Bestimmung wenigstens eines Freiraums in einem Überwachungsbereich einer Entfernungsmessvorrichtung, Entfernungsmessvorrichtung und Fahrerassistenzsystem |
US10401863B2 (en) | 2017-11-22 | 2019-09-03 | GM Global Technology Operations LLC | Road corridor |
US10303492B1 (en) | 2017-12-13 | 2019-05-28 | Amazon Technologies, Inc. | Managing custom runtimes in an on-demand code execution system |
JP6766798B2 (ja) * | 2017-12-15 | 2020-10-14 | 株式会社デンソー | 道路地図生成システム及び道路地図生成方法 |
US10303178B1 (en) | 2017-12-15 | 2019-05-28 | Waymo Llc | Collision mitigation static occupancy grid |
FR3077549B1 (fr) * | 2018-02-08 | 2023-04-14 | Psa Automobiles Sa | Procede de determination de la trajectoire d’un vehicule automobile en absence de marquage au sol. |
EP3759562B1 (fr) | 2018-03-02 | 2022-12-28 | DeepMap Inc. | Localisation faisant appel à une caméra pour véhicules autonomes |
WO2019178548A1 (fr) | 2018-03-15 | 2019-09-19 | Nvidia Corporation | Détermination d'emplacements de stationnement manoeuvrables pour véhicules autonomes |
JP7182895B2 (ja) | 2018-04-13 | 2022-12-05 | 株式会社東芝 | 情報処理装置、プログラム、および情報処理方法 |
US10726567B2 (en) | 2018-05-03 | 2020-07-28 | Zoox, Inc. | Associating LIDAR data and image data |
US10642275B2 (en) | 2018-06-18 | 2020-05-05 | Zoox, Inc. | Occulsion aware planning and control |
IT201800006594A1 (it) * | 2018-06-22 | 2019-12-22 | "Procedimento per la mappatura dell’ambiente di un veicolo, corrispondenti sistema, veicolo e prodotto informatico" | |
US11353577B2 (en) | 2018-09-28 | 2022-06-07 | Zoox, Inc. | Radar spatial estimation |
US11530921B2 (en) | 2018-09-28 | 2022-12-20 | Intel Corporation | Method of generating a collision free path of travel and computing system |
US11208096B2 (en) | 2018-11-02 | 2021-12-28 | Zoox, Inc. | Cost scaling in trajectory generation |
DE102018127990A1 (de) | 2018-11-08 | 2020-05-14 | Bayerische Motoren Werke Aktiengesellschaft | Verfahren und Verarbeitungseinheit zur Ermittlung von Information in Bezug auf ein Objekt in einem Umfeld eines Fahrzeugs |
CN111307166B (zh) * | 2018-12-11 | 2023-10-03 | 北京图森智途科技有限公司 | 一种构建占据栅格地图的方法及其装置、处理设备 |
EP3894789A4 (fr) | 2018-12-12 | 2022-07-20 | INTEL Corporation | Mappage de grille d'occupation en fonction du temps pour robots dans des environnements dynamiques |
WO2020140047A1 (fr) | 2018-12-28 | 2020-07-02 | Nvidia Corporation | Détection de distance à obstacle dans des applications de machine autonome |
US11630197B2 (en) | 2019-01-04 | 2023-04-18 | Qualcomm Incorporated | Determining a motion state of a target object |
US11475678B2 (en) | 2019-01-04 | 2022-10-18 | Qualcomm Incorporated | Lane marker detection and lane instance recognition |
US11544940B2 (en) | 2019-01-04 | 2023-01-03 | Qualcomm Incorporated | Hybrid lane estimation using both deep learning and computer vision |
US11195028B2 (en) | 2019-01-04 | 2021-12-07 | Qualcomm Incorporated | Real-time simultaneous detection of lane marker and raised pavement marker for optimal estimation of multiple lane boundaries |
US20200217950A1 (en) | 2019-01-07 | 2020-07-09 | Qualcomm Incorporated | Resolution of elevation ambiguity in one-dimensional radar processing |
US11393097B2 (en) | 2019-01-08 | 2022-07-19 | Qualcomm Incorporated | Using light detection and ranging (LIDAR) to train camera and imaging radar deep learning networks |
US11555927B2 (en) | 2019-02-05 | 2023-01-17 | Honda Motor Co., Ltd. | System and method for providing online multi-LiDAR dynamic occupancy mapping |
US11620478B2 (en) | 2019-02-06 | 2023-04-04 | Texas Instruments Incorporated | Semantic occupancy grid management in ADAS/autonomous driving |
US11016492B2 (en) | 2019-02-28 | 2021-05-25 | Zoox, Inc. | Determining occupancy of occluded regions |
US11276189B2 (en) | 2019-03-06 | 2022-03-15 | Qualcomm Incorporated | Radar-aided single image three-dimensional depth reconstruction |
DE102019205008B3 (de) | 2019-04-08 | 2020-07-02 | Zf Friedrichshafen Ag | System zur Rückwärtigen Kollisionsvermeidung |
CN111832736B (zh) | 2019-04-19 | 2024-04-12 | 伊姆西Ip控股有限责任公司 | 用于处理机器学习模型的方法、设备和计算机可读存储介质 |
US20200356415A1 (en) | 2019-05-07 | 2020-11-12 | Nutanix, Inc. | Apparatus and method for depoying a machine learning inference as a service at edge systems |
US20200378766A1 (en) * | 2019-05-31 | 2020-12-03 | Lyft, Inc. | Approaches for mapping geographic regions |
US11613254B2 (en) | 2019-06-13 | 2023-03-28 | Baidu Usa Llc | Method to monitor control system of autonomous driving vehicle with multiple levels of warning and fail operations |
CN110550029B (zh) * | 2019-08-12 | 2021-02-09 | 华为技术有限公司 | 障碍物避让方法及装置 |
US11726492B2 (en) | 2019-10-02 | 2023-08-15 | Zoox, Inc. | Collision avoidance perception system |
US11353877B2 (en) | 2019-12-16 | 2022-06-07 | Zoox, Inc. | Blocked region guidance |
WO2021120200A1 (fr) | 2019-12-20 | 2021-06-24 | Baidu.Com Times Technology (Beijing) Co., Ltd. | Procédé de lissage d'une ligne de référence sur la base d'une courbe en spirale et d'une courbe spline |
CN113223317B (zh) | 2020-02-04 | 2022-06-10 | 华为技术有限公司 | 一种更新地图的方法、装置和设备 |
CN111401208B (zh) | 2020-03-11 | 2023-09-22 | 阿波罗智能技术(北京)有限公司 | 一种障碍物检测方法、装置、电子设备及存储介质 |
EP3882813A1 (fr) | 2020-03-20 | 2021-09-22 | Aptiv Technologies Limited | Procédé de génération d'un réseau d'occupation dynamique |
EP3888988A1 (fr) | 2020-03-30 | 2021-10-06 | Aptiv Technologies Limited | Procédé et système pour déterminer une distance utilisable à l'avant d'un véhicule |
EP3905106A1 (fr) | 2020-04-27 | 2021-11-03 | Aptiv Technologies Limited | Procédé de détermination d'une zone carrossable |
EP3905105A1 (fr) | 2020-04-27 | 2021-11-03 | Aptiv Technologies Limited | Procédé pour déterminer un espace exempt de collision |
-
2020
- 2020-03-20 EP EP20164443.2A patent/EP3882813A1/fr active Pending
-
2021
- 2021-02-15 EP EP21157127.8A patent/EP3882815B1/fr active Active
- 2021-03-09 US US17/196,919 patent/US11662461B2/en active Active
- 2021-03-18 CN CN202110289238.0A patent/CN113495272B/zh active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150353083A1 (en) * | 2013-01-14 | 2015-12-10 | Robert Bosch Gmbh | Creation of an obstacle map |
US20160116916A1 (en) * | 2013-06-03 | 2016-04-28 | Robert Bosch Gmbh | Occupancy grid map for a vehicle |
Non-Patent Citations (2)
Title |
---|
FELIX KUNZ ET AL: "Fast Collision Checking with a Frenet Obstacle Grid for Motion Planning", 10. WORKSHOP FAHRERASSISTENZSYSTEME - FAS 2015, 28 September 2015 (2015-09-28), Walting im Altmühltal, pages 95 - 104, XP055725440, ISBN: 978-3-00-050746-5 * |
NUSS DOMINIK ET AL: "Consistent environmental modeling by use of occupancy grid maps, digital road maps, and multi-object tracking", 2014 IEEE INTELLIGENT VEHICLES SYMPOSIUM PROCEEDINGS, IEEE, 8 June 2014 (2014-06-08), pages 1371 - 1377, XP032620202, DOI: 10.1109/IVS.2014.6856516 * |
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